Interrupted-sampling repeater jamming with convolution kernel modulation based on digital radio frequency memory technology usually enters from the main lobe of the radar antenna, exhibiting high levels of coherence and deception in both the time and frequency domains. Therefore, it is difficult for traditional radars to identify and suppress jamming while retaining the target signal, potentially leading to problems with target detection. In recent years, since the introduction of deep learning and the implementation of end-to-end target detection for anti-jamming, the field of radar signal processing has been revolutionized. However, existing end-to-end detectors encounters the limitations in information utilization and handling hard samples, leading to performance degradation in target detection. Therefore, an attention-mechanism-based anti-jamming detector with generative adversarial training is proposed, which works directly with radar echo information in both the time and time–frequency domains. First, the conception of generative adversarial is leveraged to model the framework to mitigate the impact of hard samples on network performance by confrontation interaction. The sample equalization factor is then introduced into the cross-entropy loss function to alleviate the influence of target and jamming overlap rate on detection performance. Finally, by drawing on the attention mechanism and the idea of multiscale feature extraction, the network structure is modified to introduce time–frequency domain information and enhance the feature extraction ability of the detector, which is insensitive to variations in the jamming-to-signal ratio. Experimental results demonstrate that the proposed detector outperforms the state-of-the-art methods in terms of detection performance and robustness.
Attention-Mechanism-Based Anti-Jamming Detector With Generative Adversarial Training
IEEE Transactions on Aerospace and Electronic Systems ; 60 , 6 ; 7711-7727
2024-12-01
7541473 byte
Article (Journal)
Electronic Resource
English
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